Excess BAFF May Impact HIV-1-Specific Antibodies and May Promote Polyclonal Responses Including Those from First-Line Marginal Zone B-Cell Populations
Bibliographic record
Abstract
We have previously shown that blood levels of B-cell Activating Factor (BAFF) rise relatively to disease progression status in the context of HIV-1 infection. Excess BAFF was concomitant with hyperglobulinemia and the deregulation of blood B-cell populations, notably with increased frequencies of a population sharing characteristics of transitional immature and marginal zone (MZ) B-cells, which we defined as marginal zone precursor-like" (MZp). In HIV-uninfected individuals, MZp present a B-cell regulatory (Breg) profile and function, which are lost in classic-progressors. Moreover, RNASeq analyses of blood MZp from classic-progressors depict a hyperactive state and signs of exhaustion, as well as an interferon signature similar to that observed in autoimmune disorders such as Systemic Lupus Erythematosus (SLE) and Sjögren Syndrome (SS), in which excess BAFF and deregulated MZ populations have also been documented. Based on the above, we hypothesize that excess BAFF may preclude the generation of HIV-1-specific IgG responses and drive polyclonal responses, including those from MZ populations, endowed with polyreactivity/autoreactivity. As such, we show that the quantity of HIV-1-specific IgG varies with disease progression status. In vitro, excess BAFF promotes polyclonal IgM and IgG responses, including those from MZp. RNASeq analyses reveal that blood MZp from classic-progressors are prone to Ig production and preferentially make usage of IGHV genes associated with some HIV broadly neutralizing antibodies (bNAbs), but also with autoantibodies, and whose impact in the battle against HIV-1 has yet to be determined.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".